{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clear previously loaded data.\n",
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = r'D:\\Users\\VonBrank\\Documents\\GitHub\\code-learning\\algorithm\\deep-learning\\computer-visualization\\cs231n\\datasets\\cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside `cs231n/classifiers/linear_svm.py`. \n",
    "\n",
    "As you can see, we have prefilled the function `svm_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.812159\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "# print(X_dev.shape)\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -5.158050 analytic: -5.158050, relative error: 2.197945e-11\n",
      "numerical: 7.816105 analytic: 7.816105, relative error: 1.360948e-11\n",
      "numerical: 27.972477 analytic: 27.972477, relative error: 5.727847e-12\n",
      "numerical: 6.690528 analytic: 6.690528, relative error: 5.731086e-12\n",
      "numerical: -36.693750 analytic: -36.693750, relative error: 1.209736e-11\n",
      "numerical: 12.646738 analytic: 12.646738, relative error: 1.647438e-11\n",
      "numerical: -3.808049 analytic: -3.808049, relative error: 6.007926e-11\n",
      "numerical: 14.341153 analytic: 14.341153, relative error: 3.744046e-12\n",
      "numerical: 0.071014 analytic: 0.071014, relative error: 2.477739e-10\n",
      "numerical: -12.885690 analytic: -12.885690, relative error: 2.451731e-11\n",
      "numerical: -5.689743 analytic: -5.689743, relative error: 4.270130e-11\n",
      "numerical: 18.197190 analytic: 18.197190, relative error: 5.535670e-12\n",
      "numerical: -3.564026 analytic: -3.564026, relative error: 1.116347e-10\n",
      "numerical: 6.801054 analytic: 6.801054, relative error: 2.827450e-11\n",
      "numerical: 7.834728 analytic: 7.834728, relative error: 3.486513e-11\n",
      "numerical: 5.128870 analytic: 5.128870, relative error: 3.857241e-11\n",
      "numerical: -16.281039 analytic: -16.281039, relative error: 8.814008e-12\n",
      "numerical: 1.537671 analytic: 1.537671, relative error: 2.409858e-10\n",
      "numerical: 8.687738 analytic: 8.687738, relative error: 1.089861e-11\n",
      "numerical: -21.133932 analytic: -21.133932, relative error: 1.175136e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "vectorized_time_1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.812159e+00 computed in 0.096740s\n",
      "Vectorized loss: 8.812159e+00 computed in 0.001995s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "vectorized_time_2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.094289s\n",
      "Vectorized loss and gradient: computed in 0.001996s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss. Your code for this part will be written inside `cs231n/classifiers/linear_classifier.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "sgd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 800.490908\n",
      "iteration 100 / 1500: loss 292.928042\n",
      "iteration 200 / 1500: loss 109.279139\n",
      "iteration 300 / 1500: loss 42.965753\n",
      "iteration 400 / 1500: loss 19.210328\n",
      "iteration 500 / 1500: loss 10.533226\n",
      "iteration 600 / 1500: loss 6.970091\n",
      "iteration 700 / 1500: loss 6.210470\n",
      "iteration 800 / 1500: loss 5.644456\n",
      "iteration 900 / 1500: loss 5.037984\n",
      "iteration 1000 / 1500: loss 5.343088\n",
      "iteration 1100 / 1500: loss 5.401952\n",
      "iteration 1200 / 1500: loss 5.200839\n",
      "iteration 1300 / 1500: loss 4.953112\n",
      "iteration 1400 / 1500: loss 5.549799\n",
      "That took 10.688873s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "validate"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.377061\n",
      "validation accuracy: 0.379000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "tuning",
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 7.000000e+00 train accuracy: 0.302429 val accuracy: 0.287000\n",
      "lr 1.000000e-07 reg 2.500000e+03 train accuracy: 0.341898 val accuracy: 0.342000\n",
      "lr 1.000000e-07 reg 5.000000e+03 train accuracy: 0.370265 val accuracy: 0.388000\n",
      "lr 1.000000e-07 reg 1.000000e+04 train accuracy: 0.384735 val accuracy: 0.386000\n",
      "lr 5.000000e-07 reg 7.000000e+00 train accuracy: 0.350306 val accuracy: 0.355000\n",
      "lr 5.000000e-07 reg 2.500000e+03 train accuracy: 0.375020 val accuracy: 0.379000\n",
      "lr 5.000000e-07 reg 5.000000e+03 train accuracy: 0.362224 val accuracy: 0.343000\n",
      "lr 5.000000e-07 reg 1.000000e+04 train accuracy: 0.352857 val accuracy: 0.349000\n",
      "lr 1.000000e-06 reg 7.000000e+00 train accuracy: 0.364184 val accuracy: 0.341000\n",
      "lr 1.000000e-06 reg 2.500000e+03 train accuracy: 0.328224 val accuracy: 0.321000\n",
      "lr 1.000000e-06 reg 5.000000e+03 train accuracy: 0.339000 val accuracy: 0.351000\n",
      "lr 1.000000e-06 reg 1.000000e+04 train accuracy: 0.309735 val accuracy: 0.318000\n",
      "lr 5.000000e-06 reg 7.000000e+00 train accuracy: 0.333776 val accuracy: 0.326000\n",
      "lr 5.000000e-06 reg 2.500000e+03 train accuracy: 0.246102 val accuracy: 0.262000\n",
      "lr 5.000000e-06 reg 5.000000e+03 train accuracy: 0.261959 val accuracy: 0.244000\n",
      "lr 5.000000e-06 reg 1.000000e+04 train accuracy: 0.234469 val accuracy: 0.249000\n",
      "best validation accuracy achieved during cross-validation: 0.388000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "# Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "# Provided as a reference. You may or may not want to change these hyperparameters\n",
    "# learning_rates = [1e-7, 5e-7, 1e-6, 5e-6, 1e-5, 5e-5]\n",
    "# regularization_strengths = [2.5e3, 5e3, 7,5e3, 1e4, 2.5e4, 5e4, 7.5e4, 10e4]\n",
    "\n",
    "learning_rates = [1e-7, 5e-7, 1e-6, 5e-6]\n",
    "regularization_strengths = [2.5e3, 5e3, 7,5e3, 1e4]\n",
    "\n",
    "\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_history = svm.train(X_train, y_train, lr, reg,\n",
    "                      num_iters=1500, verbose=False)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_accuracy = np.mean(y_val == y_val_pred)\n",
    "        results[(lr, reg)] = train_accuracy, val_accuracy\n",
    "        if val_accuracy > best_val:\n",
    "            best_val = val_accuracy\n",
    "            best_svm = svm\n",
    "\n",
    "# pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "import pdb\n",
    "\n",
    "# pdb.set_trace()\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.tight_layout(pad=3)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "test"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.347000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
